As in other database systems, indexes can be used in ArangoDB to speed up data retrieval queries, sometimes by many orders of magnitude. Getting the indexes set up the right way is essential for good query performance, so this is an important topic that affects most ArangoDB installations.

ArangoDB, as a native multi-model database, competes with many single-model storage technologies. When we started the ArangoDB project, one of the key design goals was and still is to at least be competitive with the leading single-model vendors on their home turf. Only then does a native multi-model database make sense. To prove that we are meeting our goals and are competitive, we run and publish occasionally an update to the benchmark series. This time we included MongoDB, PostgreSQL (tabular & JSONB), OrientDB and Neo4j.In this post we will cover the following topics:

To investigate the impact of the Meltdown and Spectre patches on the performance of ArangoDB, we ran benchmark tests with the two storage engines available in ArangoDB (MMFiles & RocksDB). We used the arangobench benchmark and test tool for these tests.

The tests include 10 different test cases with changing test parameters like concurrency, batch requests and asynchronous execution.

The ArangoDB query language (AQL) can be used to retrieve and modify data that is stored in ArangoDB. The AQL editor in the web interface is useful for running ad hoc AQL queries and trying things out.

The editor is split into three parts. The center section allows you to write your query and modify your query bind parameters. At the bottom you can either run the query or explain it, allowing to explain the query and inspect its execution plan. This can be used to check if the query uses indexes, and which. Here more information about optimizing a query.

For the upcoming ArangoDB 3.4 release we’ve implemented a set of information retrieval features exposed via new database object `View`. The `View` object is meant to be treated as another data source accessible via AQL and the concept itself is pretty similar to a classical “materialized” view in SQL.

While we are still working on completing the feature, you can already try our retrieval engine in the Milestone of the upcoming ArangoDB 3.4 released today. You can download and give it a spin here:

Milestone ArangoDB 3.4

Geo Index Implementation

This section will cover the MMFiles based geo-index. The algorithm is optimized for in-memory accesses and optimal CPU cache utilization. The main goal for our geo queries is to reject as many distant possible result points as fast as possible.

Version 6.0.0 of the JavaScript driver arangojs is now available (Find it on GitHub).

This is a major release that introduces a small number of breaking changes so make sure to check out the arangojs changelog before upgrading. The most significant additions in this release are support for load balancing and automated failover as well as improved browser and TypeScript support.

The ArangoDB Fulltext index allows you to search for text in arbitrary strings. It is a great way to implement things like autocompletion, product searches or many other use-cases which need some form of fulltext search.The Fulltext Index is suitable for you if your use-case can be broken down to:

• Full matches of words
• Prefix matches of words
• You do not need a “ranking” of the matching documents

Starting with the mass-market availability of smartphones and continuing with IoT devices, self-driving cars ever more data is generated with geo information attached to it. Analyzing this data in real-time requires the use of clever indexing data-structures. Geo data in ArangoDB consists of 2 or more dimensions representing (x, y) coordinates on the earth surface. Searching on a single number is essentially a solved problem, but effectively searching on multi-dimensional data can be more difficult as standard indexing techniques cannot be used.There exist a variety of indexing techniques. In..

Just in time for the holidays we have a nice present for you all – ArangoDB 3.3. This release focuses on replication, resilience, stability and thus on general readiness for your production small and very large use cases. There are improvements for the community as well as for the Enterprise Edition. We sincerely hope to have found the right balance between them.